This is a coding question. Try to program a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridgc regression we try to create here has just one variable (or feature).Now that you have finished the program. What are the obscrvations and the corresponding predictions using Ridge? Now, make a plot to showoase haw well your model predicts against the observations. Use scatter plot for observations, line plot for your model predictions. Observations are in color red, and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot. You may also need to tine tune hyperparamoters such as learning rate and the number of iterations.side note- make sure the code runs successfully A1 Years Experie Salary21.131.341.55262.2729日gonne 4 5 6 7 8 9 20122824 25 25 27 28 29 30 31 2 33 弘1011121315161718192326323.3.23.73.9444.14.54.95.15.35.966.87.17.98.28.799.59.610.310.539343462053773143525398915664260150544456444557189632185579456957570816111167938660298308881363939409173898273101302113812109431105582116969112635122391121872EF import numpy as np.import pandas as pdfrom sklearn.model_selection import train_test_splitimport matplotlib.pyplot as pitRidge Regressionclass RidgeRegression ():definit__(self, learning_rate, iterations, 12_penality) :self.learning_rate learning_rateself.iterations iterationsself.12_penality 12_penalityFunction for model training.def fit( self, X, Y) 1#no_of_training examples, no_of_featuresself.m, self.n X.shapeweight initializationself. W np.zeros(self.n)self.b 0self.x = xself. Y Ygradient descent learningfor i in range ( self.iterations ) :self.update_weights ()return selfHelper function to update weights in gradient descentdef update_weights (self):#you need to figure this out.return selfHypothetical function h( x )def predict( self, X ) :return X.dot ( self.W) + self.b#Driver codedef main () 1Importing datasetdf pd. read_cav( "salary_data.csv")X df.iloc[:, :-1].valuesYdf.iloc[:, 1].values#Splitting dataset into train and test setX_train, x_test, Y_train, Y test train_test_split( X, Y,Model trainingmodel Ridge Regression ( iterations 1000,model.fit( X_train, Y_train )test_size=1/3, random_state = 0)learning_rate= 0.01, 12 penality=1)Prediction on test setY pred model.predict( x_test )print("Predicted values ", np.round( Y_pred [:3], 2))print("Real values ", Y_test [:3] )Visualization on test setif_name__ "_main_" :main ()

Ridge Regression Algorithm:1. Import necessary libraries: numpy, pandas, sklearn, matplotlib.2.…

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Database System Concepts

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ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

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This is a coding question. Try to program a Ridge regression. Please complete the coding. Note that here the data set we use has just one explanatory variable and the Ridgc regression we try to create here has just one variable (or feature). Now that you have finished the program. What are the obscrvations and the corresponding predictions using Ridge? Now, make a plot to showoase haw well your model predicts against the observations. Use scatter plot for observations, line plot for your model predictions. Observations are in color red, and predictions are in color green. Add appropriate labels to the x axis and y axis and a title to the plot. You may also need to tine tune hyperparamoters such as learning rate and the number of iterations. side note- make sure the code runs successfully

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import numpy as np.
import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as pit
Ridge Regression
class RidgeRegression ():
definit__(self, learning_rate, iterations, 12_penality) :
self.learning_rate learning_rate
self.iterations iterations
self.12_penality 12_penality
Function for model training.
def fit( self, X, Y) 1
#no_of_training examples, no_of_features
self.m, self.n X.shape
weight initialization
self. W np.zeros(self.n)
self.b 0
self.x = x
self. Y Y
gradient descent learning
for i in range ( self.iterations ) :
self.update_weights ()
return self
Helper function to update weights in gradient descent
def update_weights (self):
#you need to figure this out.
return self
Hypothetical function h( x )
def predict( self, X ) :
return X.dot ( self.W) + self.b
#Driver code
def main () 1
Importing dataset
df pd. read_cav( "salary_data.csv")
X df.iloc[:, :-1].values
Ydf.iloc[:, 1].values
#Splitting dataset into train and test set
X_train, x_test, Y_train, Y test train_test_split( X, Y,
Model training
model Ridge Regression ( iterations 1000,
model.fit( X_train, Y_train )
test_size=1/3, random_state = 0)
learning_rate= 0.01, 12 penality=1)
Prediction on test set
Y pred model.predict( x_test )
print("Predicted values ", np.round( Y_pred [:3], 2))
print("Real values ", Y_test [:3] )
Visualization on test set
if_name__ "_main_" :
main ()

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